1.0 - 8.00 - DecisionTree - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
1.0
8.00
Release Date
May 2019
Content Type
Programming Reference
Publication ID
B700-4003-098K
Language
English (United States)

The DecisionTree function takes an entire data set as training input and builds a single decision tree from it. The function creates a single decision tree in a distributed fashion, either weighted or unweighted. The model table that this function outputs can be input to the function Single_Tree_Predict.

DecisionTree requires vworkers to communicate during the tree-building process. This communication can be very expensive, depending on the number of variables and the number of possible splits; therefore, the algorithm uses a sampling approach to reduce the number of splits.

The DecisionTree function uses the functions Approximate Percentile and Percentiles to sample the split values. The split table has all the splits for all the numerical attributes that were considered in building the tree.

Helper Functions

Before calling the DecisionTree function, verify that the helper functions are installed on the ML Engine:

  • single_tree_drive
  • best_splits_by_attributes
  • best_splits_by_nodes
  • partition_data
  • percentile
  • approxPercentileMap
  • approxPercentileReduce